Dynamic perfusion magnetic resonance imaging (MRI) has demonstrated great potential for diagnosing cardiovascular and renovascular diseases. In dynamic perfusion MRI, the organ under study is scanned rapidly and repeatedly following a bolus injection of a contrast agent. Changes in pixel intensity corresponding to the same tissue across the image sequence provide valuable functional information about the organ being imaged.
Unfortunately, perfusion magnetic resonance (MR) image sequences suffer from motion induced by patient breathing during acquisition. Therefore, registration must be performed on time-series images to ensure the correspondence of anatomical structures in different frames. Due to the vast amounts of data acquired in dynamic perfusion MRI studies, which, on average, include over 100 images per scan, automatic registration is strongly desirable.
Given a sequence of perfusion MR images for the heart or the kidney, it is desirable to solve the registration problem by establishing the appropriate correspondence between every pixel in the region of interest in each frame of the sequence.
Unfortunately, this is difficult and standard block matching techniques do not work because the intensity at the same physical location changes across the MR image sequence due to the wash-in and wash-out of the contrast agent. There has been limited work on image registration to address these difficulties. An image registration algorithm that utilizes the maximization of mutual information has been proposed for cardiac MR perfusion data, and a few methods have been proposed for registration of renal MR perfusion images. These methods all require manually drawn contours in one time frame to obtain a mask or a model. This model is then used to propagate the contours to other frames in the image sequence. Thus, what is needed is automatic registration of cardiac and renal MR perfusion images.